Marketplace – AI-Driven Test Automation for API & UI TestingFinance

Our client, a leading retail mortgage lender, was constrained by time-intensive manual API and UI regression processes that limited testing frequency and delayed product feedback.

The absence of a unified automation framework, coupled with significant QA bandwidth requirements, further hindered release velocity and stability tracking. To address these challenges and strengthen their competitive position, the client sought to implement an AI-driven automation strategy designed to accelerate regression cycles, enhance test coverage, and enable proactive quality assurance across both API and UI layers.

challenge

  • Time-Consuming Manual API & UI RegressionManual API regression for just 21 endpoints took 4–12 hours, limiting regression frequency.​
  • UI testing required manual scripting and high maintenance, slowing feedback loops.​
  • Limited Automation FrameworkNo unified API/UI test automation framework existed for endpoint-level,
    schema, and visual validations.
  • High QA Effort & Delayed FeedbackTesting cycles consumed significant QA bandwidth, delaying releases
    and defect detection.
  • Inability to Quickly Track StabilityNo automated health checks to proactively track API and UI stability
    across builds.

solution

  • API Testing
    • AI-Generated Tests from Swagger: Auto-created test templates from API definitions.
    • Dynamic Change Detection: AI regenerated tests on API/schema updates without manual effort.
    • Comprehensive Regression: AI ensured coverage for endpoints, schema validations, and edge cases.​​
    • Fast Execution: Playwright + AI optimization ran 41 endpoints in <2 minutes with HTML/Allure reports.​
    • No Manual Coding: QA engineers focused on strategy, not scripting.​
  • UI Testing
    • AI-Assisted Scaffolding: GitHub Copilot & MCP auto-built scalable test structures.​
    • Maintainable Architecture: AI-recommended Page Object Model (POM).
    • AI-Generated Data: Synthetic test data and edge cases mapped to UI flows.​​​
    • CI/CD Integration: AI-driven orchestration with Allure, BrowserStack, and parallel pipelines.​

Outcomes

  • Rapid Testing at Scale: Massive time savings and increased coverage without increasing QA headcount.​
  • Reduced Production Defects: Prevention of critical leakage.
  • Shift-Left Enablement: Developers could trigger validations in CI/CD, reducing QA bottlenecks.​
  • Higher Code Quality: AI-assisted test generation ensured consistent, reusable, and maintainable scripts.
  • AI-Driven Automation Impact
    Metric / Capability Before After AI-Driven Automation
    API Regression Execution Time 2 hours for 21 endpoints < 2 minutes for 41 endpoints
    Regression Frequency Infrequent, manual only Daily automated runs
    UI Test Development Manual scripting AI-generated test cases & data
    QA Bandwidth Usage High Minimal (shift-left to developers)
    Defect Detection Time Late in cycle Early in development
    Stability Tracking Not possible Real-time health checks (API & UI)
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The AI-driven automation framework transformed our QA process. What once took days of manual effort now runs in minutes with greater accuracy and coverage. This has not only accelerated our release cycles but also given our team the confidence to innovate faster.

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